Observational learning in the motion picture market

Santugini-Repiquet, Marc, Department of Economics, University of Virginia
Spellman, Barbara, School of Law, University of Virginia
Stern, Steven, Department of Economics, University of Virginia

I focus on the market for movies released in the theater to measure the extent to which consumers learn about the quality of a movie from observing its market share in the release week. I derive the demand for movies using a dynamic discrete choice model in which consumers are endowed with private information about a movie and engage in as well as anticipate learning. I also assume that consumers watch a movie at most once to account for demand saturation.

I depart from previous applied work on estimating the demand for movies by incorporating forward-looking behavior and observational learning. I also propose a new approach to account for demand saturation. The approach allows the decay rate for a market share to depend on consumers' past decisions, past movie competition, as well as past observational learning. The decay rate also depends on consumers' anticipation of observational learning and future schedule of movies in the theater.

Given the distributional assumptions, the corresponding market shares for movies are mixing multinomial logit probabilities, taking into account consumers' forward looking behavior and heterogeneity due, in part, to their private information. Using US market-level data, I estimate the structural parameters of demand via the maxi¬ mum simulated likelihood procedure. I recover reasonable estimates for the covariates such as MPAA ratings and studio indicators.

I also find evidence of observational learning. I measure the impact of observational learning on movie demand in two different ways. First, I measure the effect of observational learning on demand in the week after the release week. For instance, I find that learning about the quality of Harry Potter: The Prisoner of Azkaban induces 1.6 million consumers to change their decisions in the week after the release week. Second, I measure the effect of anticipation of observational learning on demand in the release week. For instance, I find that an average of 1.095 million consumers de¬ cided not to watch The Matrix in its release week in order to make a more informed decision in the subsequent weeks.

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PHD (Doctor of Philosophy)
consumers, movie market, observational learning

Digitization of this thesis was made possible by a generous grant from the Jefferson Trust, 2015.

Thesis originally deposited on 2016-02-18 in version 1.28 of Libra. This thesis was migrated to Libra2 on 2017-03-23 16:33:39.

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